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Load image inpainting: An improved U-Net based load missing data recovery method

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  • Liu, Liqi
  • Liu, Yanli

Abstract

Dealing with large percentage data missing is always a challenge for load data recovery. This paper, drawing on ideas from image inpainting, formulates load missing data recovery problem as load image inpainting and the improved U-Net is proposed to restore the load image. First, the 1-dimensional load data is constructed into 2-dimensional load image. Additionally, missing data presents as irregular black holes on the load image. Then, the improved U-Net which introduces residual network (ResNet) and convolutional block attention module (CBAM) is proposed to specifically restore the incomplete load image. Meanwhile, mean absolute error (MAE) and structural similarity (SSIM) are utilized to evaluate data recovery accuracy and load pattern recovery similarity respectively. Load missing data recovery results based on the actual industrial load dataset are presented to verify the effectiveness of the proposed method.

Suggested Citation

  • Liu, Liqi & Liu, Yanli, 2022. "Load image inpainting: An improved U-Net based load missing data recovery method," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922012454
    DOI: 10.1016/j.apenergy.2022.119988
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    References listed on IDEAS

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    1. Chen, Wen & Zhou, Kaile & Yang, Shanlin & Wu, Cheng, 2017. "Data quality of electricity consumption data in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 98-105.
    2. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    3. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    4. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    5. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Missing data imputation using mixture factor analysis for building electric load data," Applied Energy, Elsevier, vol. 304(C).
    Full references (including those not matched with items on IDEAS)

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